DE19940957A1 - Traffic prognosis method for road network uses dynamic macroscopic modelling for prognosis of traffic parameters from actual traffic information - Google Patents

Abstract

The traffic prognosis method uses dynamic macroscopic modelling for prognosis of one or more traffic parameters, for evaluation of the traffic flow, using actual traffic information, with detection of the actual traffic parameters at road intersections controlled by traffic lights, during both the red and green signal lamp phases.

Description

The invention relates to a method for traffic forecasting se for a traffic network with network nodes and connecting them Routes in which the traffic at traffic-regulated network nodes time discretized during free phases and during Interrupt phases is interrupted, especially for ver traffic network areas in metropolitan areas where one or more Traffic parameters based on current traffic information based on a dynamic macroscopic modeling of the Traffic can be forecast. Under the terms "free phases" and "interruption phases" are both green phases and Red phases of traffic signal systems as well as equivalent periods to understand during which traffic is due, for example Appropriate right of way is released or stopped.

Traffic forecasting methods are gaining due to the constantly growing traffic is becoming increasingly important, especially in Connection with the increasing use of individual Route guidance systems for a person traveling on the traffic network Vehicle route recommendations for driving from a starting location give a destination and the driver while driving with a Support navigation device. As is known, the Ver traffic situation typically strong local and temporal Subject to fluctuations. Therefore the travel times for journeys on the traffic network very quickly gravie rend change. With static routing routines, which for the a journey time from a start to a destination required only based on information about the traffic situation at the start  the forecasted time can consequently be estimated deviates drastically from the travel time actually required chen. Better results can be achieved with dynamic guidance systems where one or more interests using traffic parameters such as travel time not just the traffic conditions at the time of the start, but also of timely data on the ongoing on the Predicted traffic condition per forecast period be diagnosed, d. H. it will be in the traffic forecast for the respective time during the journey for the respective Predicted traffic state of vehicle location.

The traffic forecasting methods can be roughly divided into two types divide pen, which can also be used in combination can come, namely in historical curve prognoses and dynamic traffic forecasts. Flow line forecasts are based on pattern recognition in historical traffic measurements, d. H. in Measurements of the traffic situation in the past, from which an ar typical curve lines is created. Traffic forecast from the archived waterways, the one that is selected best fits the current traffic measurement. The dynamic Traffic forecast is based on the detection of traffic objects from current traffic measurements, such as free traffic, synchroni traffic and congestion, characteristic of the investigation properties of these objects and their dynamic properties Pursuit, being for a forecast of further dynamic Development of traffic objects also a gangway pro gnose can be included.

Such procedures for historical and dynamic traffic forecast on transport networks are known in various ways, see e.g. B. the patent DE 195 26 148 C2, the published patent application DE 196 47 127 A1 and the unpublished, older German cal patent application 198 35 979.9. It is on expressways Dynamics of the traffic flow essentially through phase transitions between the different phases of traffic, d. H. the ident available traffic objects, such as free traffic, synchro  traffic and congestion, and their development, d. H. by shaped the own dynamics of the traffic objects. This dy Namely effects play in those areas of the Traffic network hardly play a role in which many traffic regulated Network nodes exist and the network node connections, d. H. the Stretch (often referred to as edges) are relatively short. This is especially the case in metropolitan areas where many Network nodes are regulated by traffic signal systems.

It is known that in this case high density traffic regulated network node who applied a queuing theory that can be the length of the queue before the traffic regulated knots, the durations of free and interruption phases, such as the red and green phases of traffic signal systems, the speed of the vehicles outside the queue, the inflows to the queue and the length of the routes in the Network are important for the dynamics of traffic, see e.g. B. the publications S. Miyata et al., "STREAM", Proc. of the 2nd Word Congress on Intelligent Transport Systems, Yokohama, Volume 1, page 289, 1995 and B. Ran and D. Boyce, "Modeling Dy namic Transportation Networks ", Springer-Verlag, Berlin, 1996.

The invention is a technical problem of providing a traffic forecasting procedure, with which one or comparatively several traffic parameters of interest casual and with the least possible computational effort can also be forecast for transport network areas, in which have a high density of traffic-regulated network nodes, the due to the alternating free and interruption phases induce time-discretized real traffic flows.

The invention solves this problem by providing a Procedure with the features of claim 1. In this procedure The traffic parameter (s) of interest are identified starting from the current at a forecast start time len traffic information based on a special dynami predicted macroscopic modeling of traffic,  which characteristically is the continuous determination effective continuous traffic flows for the transportation network stretch taking into account the rules governed by traffic Network nodes time-discretized real traffic state parameters includes. In addition to determining effective continuous strc Local traffic flows can be determined depending on the application further effective continuous traffic condition parameters, such as B. an effective continuous queue length for the respective traffic-regulated network node, based on time discretized real traffic condition parameters provided his.

The time discretized according to the invention real traffic state data determined effective continuous Lichen traffic condition parameters to which at least the Strec traffic flows can then be used instead of the time-discretized real traffic condition parameter for the further traffic forecasting steps can be used, which without significant loss of accuracy the computational effort can significantly decrease. The interested Ver Traffic parameters can be continuously for the transport network is calculated and thus also forecast. This allows the Carrying out a dynamic traffic forecast in one against negligibly small above the predefinable forecast horizon Calculation time.

In a development of the invention according to claim 2 Forecast for the traffic parameter (s) of interest Travel time, traffic situation (especially regarding the number of vehicles in and outside of queues) and / or Queue length based on the determined effec tive continuous traffic flows. This allows very reliable forecasts of the person concerned traffic parameters.

In a development of the invention according to claim 3 is a Determination of distribution coefficients, which the division  of the determined effective continuous traffic flows the network node on the routes that meet there determine, based on data from current measurements and historical Flow lines provided. This allows the traffic flow department Determine and prepare the network nodes comparatively precisely estimate.

A development of the invention according to claim 4 includes Determination of the effective continuous outflows from the Network routes based on a suitable integration of averaging over the time-discretized, real outflows and / or the determination an effective continuous number of vehicles in one queue from averaging integration over the Time-discretized real queue vehicle number. The With The duration of the message corresponds to the actual duration or one determined effective duration of a cycle of free and under refraction phase at the associated network node. For example for traffic nodes at which traffic through traffic lights or different right of way rules depending on the given given traffic-dependent input control variables, such as control room queues of vehicles and queue inflows, an effec tive free and interruption phase cycle duration based on this Input control variables determined and instead of the actual Free and interrupted phase cycle duration for the averaging procedure and if necessary also for others, from this cycle duration influenced forecast steps are used.

A development of the invention relates to claim 5 especially in the case of oversaturated queues, d. H. sol chen, the number of vehicles greater than the number of vehicles which is the queue over the node during a Can leave free phase. In this case, a medium one Waiting time for the vehicles in the queue from a Be draw determined by the effective continuous wait queues of vehicles equal to the time integral over the train effective continuous drain during the middle Waiting time is.  

In a further embodiment of the invention, according to An say 6 an average free travel time for the route section te outside of queues using an integral relationship determined according to which the free track length between two kno the time integral beyond a queue vehicle density-dependent free vehicle speed during corresponds to the average travel time, whereby the relevant Vehicle density from the quotient of the effective number of free Vehicles divided by the free route length per route lane results. Together with the configuration of the invention according to claim 5 can be a particularly reliable Rei Sea time forecast also for longer journeys in a metropolitan area manage with oversaturated queues.

In a development of the invention according to claim 7 includes the traffic forecasting algorithm special relationships between effective continuous inflows and effective continuous outflows for the various network nodes also in the case several lanes per route, between below and above saturated queues. Determining the effective continuous inflows and outflows from the time disc Realized inflows and outflows to and from the the intersecting routes enables a time-con Continuous handling of the forecasting problem and especially the Forecasting future values of interested ver sweeping parameters with good accuracy. So one looks up to it Building development of the invention according to claim 8 in the frame the traffic forecast the mathematical determination of transitions between queue oversaturation using that way determined data on the effective continuous feed and Drains and the effective continuous number of vehicles in front.

A further development of the method according to the invention Proverb 9 takes into account correlations of free phases of in egg traffic direction-sequential network  knots, such as in the case of the so-called "Greens Wave "exist, specially adapted to this situation Determination of the maximum outflow from the respective control room Snake. In the case of correlation, for undersaturated waiting snakes a higher maximum drain possible than for strewn queues.

In a further development of the method according to the invention According to Proverb 10, travel time forecasts are made for those for Legs outside of queues one by one effective continuous vehicle density dependent free driving speed and within queues one of the determined effective continuous runoff dependent driving tool speed is taken as a basis.

Advantageous embodiments of the invention are in the drawing are shown and are described below. Here demonstrate:

Fig. 1 is a graph of the time course ei nes current and a determined therefrom effective continuous outflow of vehicles from a distance of a traffic network at a verkehrsgeregel th node,

Fig. 2 is a graph corresponding to FIG. 1, but for the Fahrzeugzufluß in a distance

Fig. 3 is a graphical representation corresponding to FIG. 1, but for the number of vehicles in a queue in the case of under-saturation,

Fig. 4 is a graphical representation corresponding to FIG. 3, but for the case of supersaturation,

Fig. 5 is a section of a traffic route network, with a queue at a transport node regulated and

Fig. 6 is a representation of a traffic-controlled network node with four leading-four and opening out, in each case two-lane routes to illustrate the Verkehrsflußaufteilung at the node.

The inventive method, of which a Reali sation is explained in more detail, unless it is based on conventional Approaches are particularly suitable for traffic forecast in metropolitan areas. For the forecast are currently ge measured and historical traffic data related to one lying traffic network, z. B. a road network used and there is also a dynamic forecast of traffic for metropolitan areas with numerous network nodes where the Traffic through traffic lights or similar traffic control systems Facilities, such as special right of way regulations. The state of the traffic or one or more of them is right here presentative or derived, interesting traffic parameters such as B. the travel time, based on the current Ver traffic state based on a dynamic macroscopic mo dellation of traffic predicted. With this procedure In particular, the average travel time for each route in the Metropolitan area as the sum of the average travel times of the driving testify outside the queue and mean waiting times be predicted within the queues. The Ver Traffic forecasting methods differentiate between under-sowed queues, in which all vehicles during a Free phase can pass through the network node, and oversaturated Queues in which so many vehicles are involved that not all of them pass the network node during a free phase can.

Characteristically, this traffic forecasting method is not interrupted by the traffic control measures at the traffic-regulated network nodes and therefore, in reality, time-discrete traffic flows, i.e. the time-discriminated real outflow q _aout from a respective route or edge of the network and the time-discretized real inflow q _ain into the the respective route, and other time-discrete characteristics of the traffic, such as the current length of the queues, but effective continuous traffic condition parameters are determined, taking into account such time-discretized real traffic condition parameters, in particular effective continuous traffic flows in the form of an effective continuous outflow q _out and an effective continuous inflow q _in or out of the respective route. Depending on requirements, other effective continuous traffic parameters can be determined, e.g. B. an effective continuous length of each queue. The effective continuous traffic condition parameters determined can be continuously calculated for the traffic network regardless of the specific duration of the free and interruption phases, such as green and red phases of traffic signal systems, at the traffic-regulated network nodes and regardless of the duration of all other types of traffic interruptions at network nodes and allow a dynamic forecast to be carried out in a negligibly short time compared to the predefined forecast horizon.

The traffic forecast preferably includes the calculation of a dyna mix forecast of travel times for each route one Metropolitan area and a dynamic forecast of the length of the Queue of vehicles in front of each network node and if necessary about the traffic situation especially with regard to the number so probably the moving as well as the queues Vehicles on every stretch of the metropolitan area, the comp dynamic forecasts based on the determined effective based on continuous traffic flows.

The effective continuous runoff q _out from a stretch can be done from the relationship for this purpose

can be determined as the temporal averaging _integral over the associated current route _runoff q _aout , the integration time length T being the duration of an entire cycle of a free and an interruption _phase , e.g. B. a green and a red phase of a traffic signal system, or alternatively an effective cycle duration as the sum of the durations of an effective free phase and an effective interruption phase corresponds to the respective traffic-regulated network node. This is illustrated in FIG. 1 on the basis of an example, the current outflow q _{aout being} dashed and the effective continuous outflow q _out derived therefrom being shown as a solid line. In this example, the current outflow q _aout at the beginning of the free phase of a free and interruption phase cycle initially has a saturation value q _sat , from which it then gradually drops to a lower value for the remaining part of the free phase, which is due to an effective continuous inflow q _{in , q is} queued, which corresponds to an undersaturated situation. During the subsequent interruption phase of an overall cycle with the cycle duration T, the outflow is zero. Instead of the highly time- _discretized current outflow q _aout , the effective continuous outflow q _out derived therefrom is used for the present forecasting method, which increases in the period shown in FIG. 1.

For those network nodes at which the traffic is not through Traffic lights, but through different right of way is regulated, instead of the duration of a free or under phase a mean over the duration of some effective Free or interruption phases are used, which from the given functions of traffic, such as the number of vehicles in the queue and the inflows of vehicles into the control room snake to be determined. From this the total cycle can then duration T as an effective cycle duration in the form of the sum of the average duration derived from free and interruption phases become. In an analogous manner, for those network nodes at de automatic traffic control by means of a light signal depending on the number of vehicles in the control room  queue, from the tributaries to the queue and from others Traffic characteristics take place instead of the duration of the red or green phases the duration of effective red or green phases are used, which are determined by the fact that the strategy correspond to the traffic light control. So z. B. then the duration of the effective release or interruption phases of the Function of the number of vehicles in the queue, the vehicle inflows into the queue and other traffic files Risks correspond to those in the strategy of the light signal position control is provided. This means that traffic can be reduced dependent input control variables an effective total cycle time derived as free and interruption phase cycle duration T and be used for further forecasting purposes.

FIG. 2 shows the treatment of the vehicle inflow into the respective route analogous to the vehicle outflow according to FIG. 1 as a further traffic condition parameter. Instead of the time-discriminated, real inflow q _ain , as shown in FIG. 2 for one example by dashed lines in its course of time during three successive free and interrupt phase cycles, an effective continuous inflow q _{in is used} in the forecast, which increases for the period shown in FIG. 2.

Furthermore, the present forecasting method includes the determination of an effective continuous number of vehicles N _q in the respective queue on the basis of the real time-discriminated number of vehicles N _aq in the relevant queue according to the relationship

where T in turn the duration of the entire free and interruption phase cycle z. B. a traffic light system or an ef fective cycle referred to as the sum of an effective red and green phase at the respective network node. This determination of the effective continuous queue vehicle number N _q is illustrated in FIGS . 3 and 4 for the case of an under-saturated or an over-saturated queue.

FIG. 3 shows the case of an undersaturated queue in which the current queue vehicle number N _aq , as indicated by dashed lines in FIG. 3, increases during the period T _{R of} an interruption or red phase, and then during the period T _{G of} a subsequent free or green phase to fall back to zero.

FIG. 4 illustrates the determination of the effective continuous queue vehicle number N _q according to equation (2) above based on the current queue vehicle number N _aq for the oversaturated case. As shown in dashed lines in FIG. 4, the current number N _aq of vehicles in the queue increases again continuously during the duration T _{R of} an interruption or red phase and then falls during the duration T _{G of} a subsequent free or Green phase off again, in contrast to the undersaturated case, however not to zero, ie the queue no longer completely dissolves during a free phase. In the example shown, the current queue vehicle number N _aq rises over four consecutive free and interrupt phase cycles to an ever higher peak value at the end of the interrupt phase and during the free phases only falls to a correspondingly increasing residual value at the end of the Free phases. According to equation (2) above, this leads to the increase in the effective continuous number N _q of vehicles in the queue over the period under consideration.

With the continuous calculation according to the invention of effective continuous traffic state parameters, such as inflows and outflows to and from routes and the number of vehicles in queues, further traffic parameters relevant for a traffic forecast can then be calculated in a prognostic manner. The average waiting time t _q within a supersaturated queue at a traffic-regulated node can thus be determined from the integral relationship

for the effective continuous queue vehicle number N _q at a time t _free as a time integral over the effective continuous discharge q _out during the subsequent mean waiting time t _q . Because from this relationship can be at least numerically determined with a known effective continuous queue vehicle number N _q and known effective continuous route outflow q _out the mean waiting time t _q .

The continuity equation applies to the total effective continuous number N of vehicles on a respective route

dN / dt = q _in - q _out , (4)

which links them with the effective continuous inflow q _in and the effective continuous outflow q _{out of} the route in question. With knowledge of the above variables, the average travel time t _{free of} the vehicles for driving on a route under consideration outside the respective queue can also be determined from the relationships

determine, where v _free (ρ) denotes the speed of the vehicles outside the queue depending on the vehicle density ρ, L the length of the respective route, L _q the length of the queue on the route and n the number of lanes on the respective route. For the queue length L _q, the Be applies relationship L _q = bN _q / n, being designated b, the mean vehicle spacing in the queue.

The continuity equation applies to the effective continuous number N _q of vehicles in a supersaturated queue

dN _q / dt = q _{in, q} - q _out , (6)

with the effective continuous inflow q _{in, q} in the queue and the effective continuous outflow q _out from the queue or the route. The effective continuous inflow q _{in, q} into the queue is determined by the relationship q _{in, q} = nρv _free . For undersaturated queues, the effective queue vehicle number N _{q is} calculated from the relationship N _q = q _{in, q} t _q .

Fig. 5 illustrates the relationship between the effective continuous inflow q _{in, q} to a queue, the effective continuous outflow q _out of the same and the effective number of vehicles N _q in the queue using the example of a queue W in front of a traffic network node K on the right Lane FS of an example of a section St. which ends in node K

Fig. 6, the Verkehrsflußverhältnisse illustrated with reference to an exemplary verkehrsgere apply node on such and especially the relationship between the effective kontinuierli chen inflows q _in and the effective continuous outflows q _out for each direction lane set the opening into the node distances. The term “directional lane quantity” is to be understood in each case as the amount of all lanes that can be used by the vehicles on an equitable basis to pass through the node for onward travel in one or more assigned destination directions. So a route z. B. include a first directional lane quantity with one or more lanes, from which one can turn right or continue straight ahead, and a second directional lane quantity with one or more lanes, from which left can be turned. In way of example taken in the considered nodes of FIG. 6, four confluent lines j (j = 1,.., 4) each having two Richtungsspurmen gen k (k = 1, 2) and four opening out lines i (i = 1,.. ., 4) with two sets of direction m (m = 1, 2). Next 6 Distribution coefficient α are shown in Figure (i, m) |. Exemplified (j, k) indicative of the relative number of vehicles from the direction lane set k of the path j in the direction lane set m of the route i retract. For this purpose, the distribution coefficients are standardized so that for each directional track set k of a junction, the sum of the distribution coefficients α (i, m) | (j, k) over all directional track sets m of all outgoing routes i is equal to one. The distribution coefficients can be determined from the corresponding current measurements and historical curves. For the conditions at a traffic-regulated network node, as shown in FIG. 6, the relationship then applies to the effective continuous inflow q _in ^{(i, m)} into the directional track quantity m of the opening route i

q _in ^{(i, m)} (t) = min {q _inm ^{(i, m)} , (N _max ^{(i, m)} - N ^{(i, m)} (t)) / Δt}, (7)

where q _inm ^{(i, m) designates} an associated average effective continuous inflow, which with a corresponding average effective continuous outflow q _outm ^{(j, k)} from the respective directional track _quantity k of each entering route j via the distribution coefficients α (i, m) | (j, k) after the equation

is related. The relationship applies to the effective continuous outflow q _out ^{(j, k)} from a respective directional track amount k of an opening j

with an associated mean effective continuous outflow q _outm ^{(j, k)} , for which the relationship is different for the cases of an under- or oversaturated queue

applies in which q _{out, max} ^{(j, k)} denotes a maximum effective continuous outflow from the directional track set k of the distance j for which the relationship

q _{out, max} ^{(j, k)} = q _sat ^{(j, k)} T _G ^{(j, k)} / T ^{(j, k)} (11)

applies if there is no correlation between the free phases on the following traffic-regulated network nodes or is taken into account. T _G ^{(j, k)} denote the duration of a free phase or an effective free phase and T ^{(j, k)} = T _G ^{(j, k)} + T _R ^{(j, k)} the total duration of a free and interruption phase cycle Light signal system or other traffic control means at the network node for the directional track quantity k on the route j as the sum of the free phase duration T _G ^{(j, k)} and the interruption phase duration T _R ^{(j, k)} . With Δt is a predetermined time increment, e.g. B. in the form of a predetermined number for a time counter, and with q _sat ^{(j, k)} the saturation outflow from the queue on the directional tracks ge k of the route j, which with the saturation _outflow q _sat1 ^{(j, k)} per lane from the Queue on the directional lane quantity k of the route j in the form q _sat ^{(j, k)} = n _k q _sat1 ^{(j, k) is} in connection, where n _{k is} the number of lanes of the directional lane quantity k of the relevant confluent route j indicates. N _max ^{(i, m)} indicates a maximum number of vehicles in the directional track quantity m of the route i.

As can be seen from equation (10) above, the mean effective continuous vehicle _outflow q _outm ^{(j, k)} results from the directional track _quantity k of the route j in the unsaturated case as the minimum of the effective continuous inflow q _{in, q} ^{(j, k)} for a possible queue there and the maximum possible outflow q _{out, max} ^{(j, k)} and for the supersaturated case as a minimum of the maximum possible outflow q _{out, max} ^{(j, k)} and the quotient from the effective queue Number of vehicles N _q ^{(j, k)} divided by the time increment Δt.

If between different network nodes in a direction of travel a correlation of the free phases z. B. in the form of a "Green Wel le" applies to the maximum possible vehicle outflow q _{out, max} ^{(j, k)} from the directional track quantity k of the route j, the relationship

which differentiates the above relationship (11) according to whether there is an under-saturated or an oversaturated queue, where a predetermined number is greater than one at γ. In other words, the maximum effective continuous vehicle outflow q _{out, max} ^{(j, k)} from the directional track quantity k of the route j is higher in the case of understatement with an existing free-phase correlation than with uncorrelated free-phases of the network nodes successive in one direction of travel.

Information obtained in the context of the present traffic forecasting method about the effective continuous traffic flows and the effective continuous queue vehicle number can be used for the detection of phase transitions between undersaturated and oversaturated queues. It can be concluded that there is a transition from the undersaturated area to the oversaturated area for a directional track quantity k of a distance j if the condition

q _{in, q} ^{(j, k)} <q _out ^{(j, k)} (13)

is fulfilled, ie if the effective continuous inflow q _{in, q} ^{(j, k)} into the queue of the directional track set k on the route j becomes greater than the associated outflow q _out ^{(j, k)} from the queue. Similarly, a transition from the supersaturated area to the undersaturated area can be concluded if the conditions

q _out ^{(j, k)} ≧ q _{in, q} ^{(j, k)} and q _{in, q} ^{(j, k)} t _q ^{(j, k)} ≧ N _q ^{(j, k)} (14)

for the considered directional track quantity k of the route j are fulfilled, ie if there the effective continuous queue inflow q _{in, q} ^{(j, k) is} not greater than the effective continuous vehicle outflow q _out ^{(j, k)} and the product out the effective continuous queue inflow q _{in, q} ^{(j, k)} and the mean waiting time t _q ^{(j, k) is} at least as large as the effective number of vehicles N _q ^{(j, k)} in the relevant queue.

Based on the determined effective continuous traffic status parameters, a travel time forecast with a predicted travel time t _prog = t _free + t _{q can be carried out} for every point in time and every route of the transport network between two network nodes, for example such a journey with a virtual vehicle is simulated. For the determination of the travel time prediction is then believed that this virtual vehicle k on a respective direction lane set a route j outside of queues with a free, from the Dorti gen vehicle density ρ ^{(j, k)} dependent speed v _free ^{(j, k)} and within of queues at traffic-regulated network nodes travels at a speed which results from the effective continuous vehicle outflow q _out ^{(j, k)} determined for the location concerned and the point in time in question. The prognosticated travel time t _prog can be determined from a solution of equations (3) and (5) above. In this way, reliable travel time forecasts based on traffic condition parameters calculated continuously in a traffic network are also possible for trips in metropolitan areas in a negligibly short calculation time compared to the desired, predefined forecast horizon according to a dynamic forecast algorithm, which in turn is then used to determine travel routes with the shortest travel time can be used.

Claims (10)

1.Procedure for traffic forecast for a traffic network with traffic nodes and routes connecting these, in which the traffic at traffic-regulated network nodes is released in a time-discriminatory manner during free phases and is interrupted during interruption phases, in particular for traffic network areas in conurbations, in which

• one or more traffic parameters are predicted based on current traffic information on the basis of dynamic macroscopic modeling of the traffic,

characterized in that

• - Modeling the traffic to predict the traffic parameter or parameters, the determination of effective continuous traffic status parameters, which include at least effective continuous route traffic flows (q _in , q _out ), taking into account real traffic status parameters that are time-discretized by the free and interruption phases of the traffic-controlled network nodes (q _ain , q _aout ).

2. The method of claim 1, further characterized in that at least the travel time and / or the traffic situation the number of queues in front of traffic-regulated network nodes standing and moving outside the queues Vehicles and / or the length of the respective queue the basis of the effective continuous route traffic flows be predicted.   3. The method of claim 1 or 2, further characterized in that the division of effective continuous route traffic flows at the network nodes specifically based on the nodes assigned distribution coefficient α (i, m) | (j, k) is determined, the determined from current measurements and historical curves become. 4. The method according to any one of claims 1 to 3, further characterized in that

• - The effective continuous vehicle outflow (q _out ) from a respective route through the relationship
  is determined and / or
• - the effective continuous number N _q of vehicles in a queue in front of a respective traffic-regulated network node through the relationship
  is determined
• - Where q _{aout is} the current outflow from the route in question, N _{aq is} the current queue vehicle number and T ei ne current or effective free and interruption phase cycle duration for the relevant network node.

5. The method according to claim 4, further characterized in that the mean waiting time t _q in a supersaturated queue in front of a traffic-regulated network node from a respective vehicle arrival time t _free based on the associated effective continuous outflow q _out and the associated effective number of vehicles N _q in the queue the relationship
is determined. 6. The method according to claim 4 or 5, further characterized in that the mean free travel time t _free for a respective route outside an associated queue from the relationships
is determined, where N is the total effective number of vehicles on the route, n the number of lanes on the route, L the length of the route and L _q = bN _q / n the queue length with b as the mean vehicle distance in the queue and v _free that vehicle density-dependent speed of vehicles outside the queue. 7. The method according to any one of claims 1 to 6, further characterized in that the effective continuous inflow q _in ^{(i, m)} and the effective continuous outflow q _out ^{(j, k)} for each direction trace amount m or k of a respective distance i or j at a respective traffic-regulated network node through the relationships
are given, where N is the total number of vehicles in a respective directional lane quantity and α (i, m) | (j, k) distribution coefficients which indicate the relative number of vehicles which move from a directional lane quantity k of a route j into a directional lane quantity m drive a route i, while q _sat a queue-saturation discharge of the respective route, T _G the duration of a free phase and T the duration of a free and interruption phase cycle and Δt denote a predetermined time increment. 8. The method according to claim 7, further characterized in that

• - The phase transition from an under-saturated to an over-saturated queue by the condition
  q _{in, q} ^{(j, k)} <q _out ^{(j, k)}
  from the comparison of the effective continuous queue inflow q _{in, q} with the effective continuous queue outflow q _out and / or
• - the phase transition from a supersaturated to an under-saturated queue due to the conditions
  q _out ^{(j, k)} ≧ q _{in, q} ^{(j, k)} and q _{in, q} ^{(j, k)} t _q ^{(j, k)} ≧ N _q ^{(j, k)}
  from the comparison of the effective continuous queue inflow q _{in, q} and the effective continuous queue outflow q _out and the comparison of the product of mean waiting time t _q and effective continuous inflow q _{in, q} with the effective continuous queue vehicle number N _{q is} determined.

9. The method of claim 7 or 8, further characterized in that

• - Is queried whether there is a correlation between free phases at different traffic-regulated network nodes in a direction of travel, and
• - In the case of an existing free-phase correlation, the maximum effective continuous queue vehicle outflow q _out ^{(j, k)} for each directional track set k of a respective route j through the relationship
  is given, where γ is a predetermined number greater than one.

10. The method according to any one of claims 1 to 9, further characterized in that a travel time forecast for a journey from a starting point to egg nem destination is performed, in which the journey is simulated by a vir tual vehicle that outside of queues with a vehicle density-dependent free speed (v _free ) and within queues at a speed determined by the effective continuous queue vehicle outflow (q _out ) determined speed.